Automatic Target Recognition (ATR) methods and systems generally use remote sensors or image-capturing devices to track and affirmatively identify a particular target object. The integrity of the match between the detected object and the target object is of the utmost importance in most ATR applications, including, primarily, most military applications. In order to accurately determine that the object being tracked is in fact the target object, the signal processed via the remote sensor must contain the maximal amount of relevant information. The inevitable presence of noise corrupts and limits such information.
As such, conventional methods and systems are adapted to suppress or remove any noise in the image or the image signal. For example, in 2D image processing systems that convert each pixel of the captured image into a needle or normal vector representation of the visible surface at that particular image location. A 2.5D normal(s) or needle map, herein referred to as a “normal map” is defined as a type of perspective image wherein each pixel represents a 3-D surface normal. The normal map may be compactly represented by the notation n(r), where n ε 3 is a vector representing the 3-D surface normals, and r  2 is a vector representing the 2-D pixel location in the image plane. Typically, the coordinate system for representing the surface normals is such that the {circumflex over (x)} and ŷ directions coincide with the image plane, while the {circumflex over (z)} direction is perpendicular to the image plane. However, the captured image includes noise, and thus, the normals vectors include inaccuracies.
A standard method for suppressing noise is to apply a liner filter designed to leave frequencies with more signal than noise unchanged, while suppressing frequencies with more noise than signal. While this method is effective in noise reduction, the method results in unacceptable signal degradation. For example, suppressing noise in images in this way usually blurs edges because the filter combines sample values from both sides of the edge, giving an intermediate result. The blurring is noticeable and offensive to human viewers.
An alternative noise suppression method that avoids blurring involves the use of a median filter. The median filter receives an input signal, and for each location x of the given input signal (a pixel in the case of an image), the median filter replaces the value at x with the median of the previously identified or original sample values in a neighborhood of x. Most filters, including linear and median filters, use the same values in a neighborhood of x to compute the new value of x. An analogous neighborhood (i.e., one having the same size and shape) may be used for every location in the signal. The “size and shape” of a neighborhood is often referred to as the filter's “region of support.”
These conventional filters identify the median of a set of vectors by taking the element of the set that has the smallest summed distance to the other vectors in the set. Although the conventional median filters avoid blurring edges because the median has the same value as one of the samples, such filters are limited because the median represents a scalar value.
Furthermore, this approach is inefficient because there may not be a choice of vector that is in the middle of the others in the set. As shown in FIG. 1, the neighborhood of sample includes three two-dimensional vectors (labeled A, B, and C) arranged close to the vertices of an equilateral triangle, such that neither vector A, B, or C is the obvious choice for the median. However, if one vector is slightly closer to the triangle's center (denoted by the “X” in FIG. 1) than the others (vector A in FIG. 1), the conventional median filter would select vector A as the median. Thus, this conventional median filter is very sensitive to small perturbations of the vectors, which can lead to undesirable effects.
Moreover, in the case of a 2.5D image of surface normals, the vectors on the edge where two surfaces join can flop back and forth between the normals of the two surfaces as one moves along the edge, making the edge appear to be jagged, when it should be smooth. Similarly, color images filter by conventional median filters also suffer from this type of inaccuracy, wherein a smooth edge becomes jagged. In addition, the conventional median filter requires significant computation resources for operation.
Furthermore, many ATR applications require that two 2.5D normal maps be matched to determine if they represent the same object. Precise matching requires that the two normal maps undergo registration process. Registration of two images, taken at different times, and/or by different sensors, is the process of aligning the images such that they coincide, according to some well-defined criteria. However, conventional ATR systems and methods lack a technique or process for the registration of 2.5-D normal maps.
Accordingly, there is a need in the art for an efficient method and system for determining an optimized vector median and registration of 2.5D normal maps in order to generate improved matching of 2.5D normal maps in object recognition applications.